Data can be stored in a way that represents relationships between factual entities in a graph. Data stored in this form is sometimes referred to as resource description framework data (or RDF data). RDF data is often referred to as a graph that includes a set of triples, wherein each triple includes a subject, a predicate, and an object. This type of triple can be thought of as a directed-arc diagram in which each triple is represented as a node-arc-node link. Each triple represents a statement of a relationship between the things denoted by the nodes in that link. The subject and object are represented by the nodes and the predicate is represented by the directed link. Links are sometimes referred to as edges. These edges or links are labeled and links with different labels have different meanings. The directionality of the link is also significant, in that it always points toward the object. Two exemplary items that can be represented by a graph of triples are:    “Maui is located in the Pacific Ocean”; and    “Maui is an island”.    The triple representing the first fact includes “Maui” and “Pacific Ocean” as nodes and a link labeled “location” pointing from Maui to Pacific Ocean.
A rule is a system by which a new triple can be inferred based on existing triples. With reference to the examples given above, a rule might be “if some object is located in the Pacific Ocean and that object is an island, it can be inferred that the object is a “Pacific island”.
Semantic reasoning systems allow a user to execute logical queries against a graph of triples in order to discover new information. For example, some semantic reasoning engines are implemented using the Prolog language, which is a general purpose logic programming language associated with artificial intelligence and computational linguistics, or the Datalog language, which is a query and rule language for deductive data stores that syntactically is a subset of Prolog. These two are only exemplary languages which may be implemented in a semantic reasoning engine, and others are used as well.
In systems where semantic queries (that is, queries that are dependent for their execution upon the execution of semantic reasoning or which require the calling and application of semantic rules) are executed, a semantic reasoning engine is often deployed between a user that provides a query, and a relational data store. The relational data store contains the facts and relationships either in the form of triples, as discussed above, or in a form from which such triples can be inferred. These types of systems use the semantic reasoning engine to encode and execute rules and provide a query language that can be used by a user to access data that is either stored in the form of triples or stored in a form from which the triples can be inferred.
However, such systems suffer from a number of drawbacks. The semantic reasoning engines are not easily extensible or scaleable. In addition, while parsing the input query and executing numerous queries against the relational data store, the semantic reasoning engines often materialize large datasets which take up a great deal of memory. Similarly, while the semantic reasoning engine is generating the desired results, it must perform its own memory and caching management.
The discussion above is merely provided for general background information and is not intended to be used as an aid in determining the scope of the claimed subject matter.